Sensor devices, and/or more particularly “smart” sensor systems, typically combine a sensing element, analog interface circuit, an analog-to-digital converter (ADC), and a bus interface, all-in-one housing, and may include additional functionality, such as self-testing, self-identification, self-validation, or self-adaptation, by way of example. The single-housing sensor system may also be configured to perform signal processing and multi-sensing capabilities. Typical configurations of these sensors may include an analog front end (AFE), analog-to-digital converter (ADC), and digital signal processing (DSP) circuit and control.
The AFE module typically includes a front-end signal conditioning circuit, a programmable gain amplifier (PGA) and an analog filter. A post-processing DSP circuit may include different types of digital filters. Due to silicon deep sub-micron manufacturing process limitations for devices (e.g., sensors), and the precision requirements of the circuit design specifications for different industry standards, the analog and digital circuits implemented into these sensor devices or smart sensor system modules often include dedicated calibration circuits and common system interfaces for calibration purposes.
Conventional sensor calibration at a circuit level may typically require a dedicated memory component, such as a non-volatile EEPROM, to store predefined calibration coefficients, and/or other calibration parameters. These calibration parameters may also include AFE programmable gain coefficients, as well as calibration configuration and calibration output data. Some analog filters may also be calibrated with predefined coefficient settings through the system calibration interface. Similarly, DSP circuits may include different digital filters such as a finite impulse response (FIR) or Infinite Impulse Response (IIR) circuits, which can also be implemented with the system calibration interface. Using this configuration, calibration coefficient settings can thus be loaded into the dedicated memory storage.
Calibration of specific-use sensors may be costly and inefficient, as it may typically require individual calibration of each sensor geared to the specific use. A generic sensor calibration platform may be advantageous for accommodating various types of calibration parameters, particularly in cases where large quantities of sensor devices are produced in a manufacturing process. However, calibration platforms may involve the calibration of multiple different types of sensors, wherein each type of sensor may require multiple different predefined calibration coefficient settings, calibration configurations, and calibration output. Such manufacturing process configurations may require very large amounts of dedicated sensor calibration parameter settings and storage areas. Furthermore, incremental accumulation and periodic updates of the sensor calibration parameter settings back to the calibration system would be costly to implement using strictly conventional manufacturing calibration systems. As such, apparatuses, systems and methods are needed to improve the effectiveness and efficiency of programming and updating calibration parameters for sensors in a scalable environment.